Chance-Constrained Surgery Planning Under Conditions of Limited and Ambiguous Data

DOI10.1287/ijoc.2018.0835zbMath1451.90075OpenAlexW3124440843MaRDI QIDQ5139613

Siqian Shen, Yan Deng, Brian T. Denton

Publication date: 9 December 2020

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.2018.0835

zbMATH Keywords

schedulingchance-constrained programmingmixed integer linear programmingbranch-and-cutallocationdistributionally robust optimization\( \phi \)-divergence

Mathematics Subject Classification ID

Mixed integer programming (90C11) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Linear programming (90C05) Management decision making, including multiple objectives (90B50) Deterministic scheduling theory in operations research (90B35) Robustness in mathematical programming (90C17)

Related Items (8)

Current trends in operating room scheduling 2015 to 2020: a literature review ⋮ Logic-Based Benders Decomposition and Binary Decision Diagram Based Approaches for Stochastic Distributed Operating Room Scheduling ⋮ Appointment scheduling for multi-stage sequential service systems with limited distributional information ⋮ A value function-based approach for robust surgery planning ⋮ Data-driven distributionally robust surgery planning in flexible operating rooms over a Wasserstein ambiguity ⋮ Chance-constrained optimization under limited distributional information: a review of reformulations based on sampling and distributional robustness ⋮ Scenario Grouping and Decomposition Algorithms for Chance-Constrained Programs ⋮ Stochastic radiotherapy appointment scheduling

Cites Work

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• Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty
• A Sample Approximation Approach for Optimization with Probabilistic Constraints
• Mixing mixed-integer inequalities

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